Support vector data description (SVDD) is a machine-learning technique used for single class classification and outlier detection. SVDD formulation with a kernel function provides a flexible data description around data. The value of kernel function parameters affects the nature of the data boundary.
The SVDD of a dataset is obtained by solving a quadratic programming problem. The time required to solve the quadratic programming problem is directly related to the number of observations in the training dataset resulting in a very high computing time for large training datasets.